Audio
whispering-ui
Native desktop UI for live Whisper transcription and translation.
★ 329 estrellasMIT
Ver la guía →WhisperLive es un proyecto de audio de código abierto de collabora (collabora/WhisperLive en GitHub). Tiene 4.309 estrellas en GitHub y 603 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 7 de octubre de 2026.
WhisperLive: a nearly-live OpenAI Whisper transcription server for files, mics, and streams
Decide which backend fits your hardware — faster-whisper (default), TensorRT, or OpenVINO — and your client types.
Run the setup script for PortAudio, create a Python 3.12 environment, and pip-install whisper-live.
Launch run_server.py on a port with your client and connection limits.
Send a file with the client script, or use the Python TranscriptionClient from your own code.
faster-whisper, TensorRT, and OpenVINO backends for different hardware.
Word-level timestamps and confidence scores, not just plain text.
Optional diarization via pyannote.audio identifies who spoke when.
Bias recognition toward custom terms and names.
Transcribe files, live microphone input, raw PCM, and RTSP/HLS streams; batch inference is supported.
An --enable_rest flag exposes an OpenAI-style transcription API alongside the streaming protocol.
Clone the project and enter its directory.
git clone https://github.com/collabora/WhisperLive.git
cd WhisperLiveInstall system audio dependencies, then the Python package (in a Python 3.12 venv).
bash scripts/setup.sh
pip install whisper-liveRun the transcription server with the faster-whisper backend.
python3 run_server.py --port 9090 --backend faster_whisper --max_clients 4 --max_connection_time 600Collabora, the open-source consultancy, maintains WhisperLive.
Yes, MIT-licensed; Whisper models run locally on your hardware.
No cloud key is needed. Optional speaker diarization uses pyannote.audio models from Hugging Face, which may require accepting model terms there.
The API is a managed per-minute service; WhisperLive is a self-hosted streaming server with multiple local backends, diarization, and stream inputs.
Run `bash scripts/setup.sh`, `pip install whisper-live`, start `run_server.py`, then send audio with `run_client.py --files <file>`.
Native desktop UI for live Whisper transcription and translation.
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Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
Port of OpenAI's Whisper model in C/C++
Los demás proyectos de Audio con más estrellas del catálogo de LibreAI: la lista realista de alternativas si WhisperLive no es lo que buscas.
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Robust Speech Recognition via Large-Scale Weak Supervision
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
Port of OpenAI's Whisper model in C/C++
Proyectos de Audio cercanos en el catálogo, los más próximos en estrellas a WhisperLive: sigue explorando a ambos lados.
MOSS-TTS-Nano: a 0.1B open multilingual speech model that clones voices and streams in real time on CPU
MetaVoice-1B: a 1.2B-parameter foundation model for emotional, zero-shot text-to-speech
FastRTC turns any Python function into a real-time audio or video stream over WebRTC or WebSockets
MOSS-TTS family: open text-to-speech models for voice cloning, dialogue, voice design, and real-time agents
Turn the beloved Kokoro voice model into an OpenAI-compatible speech API with one Docker command.
Converts text to speech in realtime
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.